Docs: Fix errors in docs and dosctrings

segrelab · Sep 21, 2023 · 208a61f · 208a61f
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diff --git a/README.md b/README.md
@@ -16,7 +16,7 @@ It incorporates various popular, publicly available tools as well as custom Pyth
 
 The MiCoNE framework is introduced in:
 
-Kishore, D., Birzu, G., Hu, Z., DeLisi, C., Korolev, K., &amp; Segrè, D. (2023). Inferring microbial co-occurrence networks from AMPLICON DATA: A systematic evaluation. mSystems. doi:10.1128/msystems.00961-22.
+Kishore, D., Birzu, G., Hu, Z., DeLisi, C., Korolev, K., &amp; Segrè, D. (2023). Inferring microbial co-occurrence networks from amplicon data: A systematic evaluation. mSystems. doi:10.1128/msystems.00961-22.
 
 Data related to the publication can be found on Zenodo: https://doi.org/10.5281/zenodo.7051556.
 
@@ -70,7 +70,7 @@ Supported subcommands:
 3. `clean` - Cleans files from a pipeline run (cleans temporary data, log files and other extraneous files)
 4. `validate-results` - Check the results of the pipeline execution
 
-### Initializing the environments
+### Installing the environments
 
 In order to run the pipeline various `conda` environments must first be installed on the system.
 Use the following comand to initialize all the environments:

diff --git a/README.rst b/README.rst
@@ -19,7 +19,7 @@ scripts to facilitate inference of co-occurrence networks from 16S data.
 The MiCoNE framework is introduced in:
 
 Kishore, D., Birzu, G., Hu, Z., DeLisi, C., Korolev, K., & Segrè, D.
-(2023). Inferring microbial co-occurrence networks from AMPLICON DATA: A
+(2023). Inferring microbial co-occurrence networks from amplicon data: A
 systematic evaluation. mSystems. doi:10.1128/msystems.00961-22.
 
 Data related to the publication can be found on Zenodo:

diff --git a/docs/conf.py b/docs/conf.py
@@ -72,7 +72,7 @@
 #
 # This is also used if you do content translation via gettext catalogs.
 # Usually you set "language" from the command line for these cases.
-language = None
+language = "en"
 
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.

diff --git a/docs/example_pipeline.rst b/docs/example_pipeline.rst
@@ -0,0 +1,50 @@
+=====================================
+Example pipeline setup and execution
+=====================================
+
+Preliminary setup
+-----------------
+
+Before execution of the ``MiCoNE`` pipeline we need to install the environments:
+
+.. code:: sh
+
+    micone install
+
+.. note:: This command will take a considerable amount of time (several hours) as MiCoNE will install all the ``conda`` environments
+
+If you wish to install only a subset of the environments, you can specify the environments to install using the ``-e`` option:
+
+.. code:: sh
+
+    micone install -e <env1>
+
+The list of supported environments can be found in the :ref:`supported_environments` section.
+
+- micone-cozine
+- micone-dada2
+- micone-flashweave
+- micone-harmonies
+- micone-mldm
+- micone-propr
+- micone-qiime2
+- micone-sparcc
+- micone-spieceasi
+- micone-spring
+
+
+Network inference workflow
+---------------------------
+
+Install the micone conda environment.
+Run micone install -e <env> to install all the sub-environments that you need
+Run micone init -w full -o <pipeline_dir> to set up the pipeline template. This creates a nextflow workflow template for the full pipeline.
+Replace the existing main.nf,nextflow.config and samplesheet.csv files with the ones that I have attached to this email. You will also need to modify some of the files in the pipeline_dir according to these instructions: https://github.com/segrelab/MiCoNE/tree/master#configuration-and-the-pipeline-template
+Finally, you can run the pipeline using bash run.sh or qsub run.sh (if running on the cluster)
+Note that this workflow is only valid if your biom files already have taxonomy labels assigned. You must run the workflow from the TA step if they do not. Let me know if this is the case.
+
+Other useful references:
+All the supported configuration options in table format: https://github.com/segrelab/MiCoNE-pipeline-paper/tree/master/tables/csv
+Actual default configuration files used during program execution (copied over when you use the micone init -w full -o <pipeline_dir> command: https://github.com/segrelab/MiCoNE/tree/master/micone/pipelines/configs
+Here are example configuration files that were used in the analysis performed for the paper: https://github.com/segrelab/MiCoNE-pipeline-paper/tree/master/scripts/runs
+
diff --git a/docs/index.rst b/docs/index.rst
@@ -19,7 +19,7 @@ scripts to facilitate inference of co-occurrence networks from 16S data.
 The MiCoNE framework is introduced in:
 
 Kishore, D., Birzu, G., Hu, Z., DeLisi, C., Korolev, K., & Segrè, D.
-(2023). Inferring microbial co-occurrence networks from AMPLICON DATA: A
+(2023). Inferring microbial co-occurrence networks from amplicon data: A
 systematic evaluation. mSystems. doi:10.1128/msystems.00961-22.
 
 Data related to the publication can be found on Zenodo:
@@ -55,6 +55,7 @@ Table of Contents
 
    installation
    usage
+   example_pipeline
    data_import_export
    modules
    contributing

diff --git a/docs/micone.pipelines.rst b/docs/micone.pipelines.rst
@@ -12,39 +12,6 @@ micone.pipelines.command module
    :undoc-members:
    :show-inheritance:
 
-micone.pipelines.helpers module
--------------------------------
-
-.. automodule:: micone.pipelines.helpers
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-micone.pipelines.pipeline module
---------------------------------
-
-.. automodule:: micone.pipelines.pipeline
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-micone.pipelines.process module
--------------------------------
-
-.. automodule:: micone.pipelines.process
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-micone.pipelines.template module
---------------------------------
-
-.. automodule:: micone.pipelines.template
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-
 Module contents
 ---------------
 

diff --git a/docs/micone.rst b/docs/micone.rst
@@ -7,7 +7,6 @@ Subpackages
 .. toctree::
    :maxdepth: 4
 
-   micone.config
    micone.conversion
    micone.logging
    micone.main

diff --git a/docs/usage.rst b/docs/usage.rst
@@ -34,7 +34,9 @@ Supported subcommands:
    log files and other extraneous files)
 4. ``validate-results`` - Check the results of the pipeline execution
 
-Initializing the environments
+.. _supported_envs:
+
+Installing the environments
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 In order to run the pipeline various ``conda`` environments must first
@@ -52,6 +54,7 @@ Or to initialize a particular environment use:
    micone install -e "micone-qiime2"
 
 The list of supported environments:
+
 - micone-cozine
 - micone-dada2
 - micone-flashweave
@@ -73,8 +76,12 @@ co-occurrence networks):
 
    micone init -w <workflow> -o <path/to/folder>
 
-Other supported pipeline templates are (work in progress): - full - ni -
-op_ni - ta_op_ni
+Other supported pipeline templates are (work in progress):
+
+- full
+- ni
+- op_ni
+- ta_op_ni
 
 To run the pipeline, update the relevant config files (see next
 section), activate the ``micone`` environment and run the ``run.sh``
@@ -159,7 +166,7 @@ The parameters for the pipeline execution are in the ``micone/pipelines/configs/
 The following tables contain the list of default parameters for each step of the pipeline:
 
 Sequence Processing (SP)
-^^^^^^^^^^^^^^^^^^^^^^^
+~~~~~~~~~~~~~~~~~~~~~~~~~
 
 +---------------------+----------------+--------------------------------+---------------------------+------------------+
 | Step                | Task           | Tool                           | Parameter                 | Value            |
@@ -195,7 +202,7 @@ Sequence Processing (SP)
 
 
 Denoising and Clustering (DC)
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 +------------------+------------------+---------------------+-------------+
 | Task             | Tool             | Parameter           | Value       |
@@ -237,7 +244,7 @@ Denoising and Clustering (DC)
 
 
 Taxonomy Assignment (TA)
-^^^^^^^^^^^^^^^^^^^^^^^^
+~~~~~~~~~~~~~~~~~~~~~~~~
 
 +--------+-------------+---------------+----------------------+
 | Task   | Tool        | Parameter     | Value                |
@@ -261,7 +268,7 @@ Taxonomy Assignment (TA)
 
 
 OTU Processing (OP)
-^^^^^^^^^^^^^^^^^^^
+~~~~~~~~~~~~~~~~~~~
 
 +-----------+--------------------+-------------------+--------------------------------------------------------------+
 | Task      | Tool               | Parameter         | Value                                                        |
@@ -289,7 +296,7 @@ OTU Processing (OP)
 
 
 Network Inference (NI)
-^^^^^^^^^^^^^^^^^^^^^^
+~~~~~~~~~~~~~~~~~~~~~~
 
 +-------------+------------------------------+-----------------------+--------------+
 | Task        | Tool                         | Parameter             | Value        |

diff --git a/micone/main/network.py b/micone/main/network.py
@@ -11,20 +11,19 @@
 import simplejson
 from statsmodels.stats.multitest import multipletests
 
-from . import Lineage
 from ..validation import (
-    InteractionmatrixType,
-    CorrelationmatrixType,
-    PvaluematrixType,
-    ObsmetaType,
-    MetadataModel,
     ChildrenmapType,
-    NodesModel,
+    CorrelationmatrixType,
+    ElistType,
+    InteractionmatrixType,
     LinksModel,
+    MetadataModel,
     NetworkmetadataModel,
-    ElistType,
+    NodesModel,
+    ObsmetaType,
+    PvaluematrixType,
 )
-
+from . import Lineage
 
 DType = List[Dict[str, Any]]
 LinkDType = Tuple[str, str, Dict[str, float]]
@@ -76,12 +75,6 @@ class Network:
     ----------
     graph : Union[nx.Graph, nx.DiGraph]
         The networkx graph representation of the network
-    nodes : DType
-        The list of nodes in the network and their corresponding properties
-    links : DType
-        The list of links in the network and their corresponding properties
-    metadata : Dict[str, Any]
-        The metadata for the network
 
     Examples
     --------

diff --git a/micone/main/network_group.py b/micone/main/network_group.py
@@ -37,12 +37,6 @@ class NetworkGroup(Collection):
     ----------
     graph : Union[nx.MultiGraph, nx.MultiDiGraph]
         The networkx multi-graph representation of the network
-    nodes: DType
-        The list of nodes in the network group
-    links: DType
-        The list of links in the network group
-    contexts: DType
-        The list of all contexts in the network group
     """
 
     def __init__(self, networks: List[Network], id_field: str = "taxid") -> None:

diff --git a/micone/main/otu.py b/micone/main/otu.py
@@ -35,12 +35,6 @@ class Otu:
     ----------
     otu_data : biom.Table
         OTU counts table in the `biom.Table` format
-    sample_metadata : pd.DataFrame
-        Metadata for the samples
-    obs_metadata :  pd.DataFrame
-        Lineage data for the observations (OTUs)
-    tax_level : str
-        The taxonomy level of the current Otu instance
 
     Notes
     -----